Searching information on web pages is a tedious task for
users as web servers provide complete web pages and do not tailor their
content to the user's current information need. This leaves an enormous amount
of workload for the user and influences his emotional attitude towards
the whole task even if a search engine has filtered pages that are relevant to
a user query. In this paper, we propose an approach to adapt the response to
queries to user preferences for his reading experience in order to leverage the
problem of information overload. With these preferences, it is possible to
select the most preferred content from a web page. In our view, the
preferences are a quantitative way to express conversational maxims. We present
our experimental approach to learn these preferences from annotated browsing
sessions and introduce a decision strategy for the selection of content on the
basis of the learned preferences.